The usage of medical imaging devices to diagnose and plan treatment for various internal ailments is well known. Often, an imaging device such as an X-ray device, Computer Tomography (CT), or Magnetic Resonance Imaging (MRI) device is used to generate one or more initial scans or images of the area of interest. Typically, once an image has been acquired, critical structures (e.g., regions or organs) disposed in the target area are specifically identified so that treatment may be optimally directed. Conventional medical imaging techniques include techniques for automatically identifying (“segmenting”) organs and large structures. These techniques often include delineating adjacent structures by derived radiodensities and classifying the structures according to their relative positions and derived densities with known values. However, even an automatic process for segmentation of anatomical structures can be computationally expensive and time-consuming.
One approach to reduce the time and resources involved in labeling structures of interest in a medical image is to utilize an annotated template image, or “atlas.” An atlas is an image (a tomography image, in the case of a medical image) that has been segmented previously and whose structures of interest (e.g., anatomical structures) have been labeled. Typically, an atlas image is selected from a number of potential atlas images via a matching process with the target image (e.g., current patient image), where the matching process includes deformable registration of the target image with each potential atlas image in order to make a comparison. Deformable registration is a process of establishing a spatial correspondence between (at least two) images using image data (e.g., pixel intensity values), bringing both images into a similar geometric framework in order to more accurately compare similar features within the images. For example, the deformation may include a deformation vector field, whereby a first image (e.g., the target image) is transformed in order to compare (e.g., match) against a potential atlas image. The matching process may, following the deformable registration, rank potential atlas images according to some criteria, and determine the atlas to be that potential image which is ranked most highly according to the chosen ranking criteria.
For atlas-based automatic segmentation, an atlas is used as a template image to aid in structure identification and labeling in a target image, such that structures of interest in the new image may be readily labeled and a treatment plan for the new patient may be expedited. An atlas image may also have radiation dosimetry and geometric planning information associated with its labeled structures.
It is appreciated by those of skill in the art that the particular atlas chosen for use in atlas-based automatic segmentation has a substantial impact on the final segmentation results for the new image. While atlas-based automatic segmentation offers efficiency improvements for labeling a new patient image and developing a radiation treatment plan, the process of selecting an optimal atlas for a new patient image from a set of potential atlas template images can still be significantly time-consuming and labor intensive. The time and labor increases with an increasing number (as well as an increasing data-density) of potential atlas template images, each of which requires manipulation according to the above, in order to find an optimal atlas for automatic segmentation.